Main experimental results.
We show the main experimental results on CIFAR-10-C and ImageNet-100-C in Table REF .
First of all, we observe that \(\operatorname{\mathbf {\ell }}_p\)  adversarial training is a strong baseline on common corruptions on both datasets with a larger gain on CIFAR-10-C.
Using our proposed relaxed LPIPS adversarial training further improves the corruption accuracy on both datasets: from 74.6% to 84.1% on CIFAR-10-C and from 47.5% to 48.8% compared to standard models. Moreover, RLAT also improves calibration compared to the standard model: from 16.6% to 9.9% ECE on CIFAR-10-C and from 10.0% to 9.1% ECE on ImageNet-100-C.
We also observe that 100% Gaussian augmentation even deteriorates the performance on ImageNet-100-C
while 50% Gaussian augmentation significantly improves the average accuracy which is consistent with [1]}.
